Accelerating siena07() with cusna

Besides the standalone estimator (vignette("cusna")), cusna can serve as an acceleration backend for RSiena: the native simulator replaces RSiena’s inner simulation loop, while RSiena keeps its full Robbins–Monro estimation machinery — and hence its convergence behavior. Nothing in RSiena is forked or patched.

This is a useful path for hard, nearly collinear specifications, where a standalone method-of-moments run may not converge from a cold start: RSiena supplies convergence, cusna supplies speed.

The code below is shown but not run here (it needs the RSiena package).

The FRAN hook

RSiena’s siena07() calls a simulation function named FRAN once per Robbins–Monro iteration. If alg$FRAN is a function, RSiena uses it directly. cusna_fran() builds such a function over the native simulator:

library(cusna)
library(RSiena)

# ... assemble `dat` (sienaData) and `eff` (sienaEffects) as usual ...

alg <- sienaAlgorithmCreate(projname = NULL, cond = FALSE)

alg$FRAN <- cusna_fran(
  waves        = list(w1, w2, w3),                  # the same 0/1 wave matrices
  effect_names = c("density", "recip", "transTrip"),# in the RSiena effects order
  conditional  = FALSE)                             # must match `cond`

ans <- siena07(alg, data = dat, effects = eff, useCluster = FALSE)
ans   # a normal sienaFit: estimates, standard errors, convergence as usual

The effect_names must list the included effects in the same order as the rows of the RSiena effects object, and conditional must match the algorithm’s cond setting. Covariate effects (egoX/altX/simX/sameX) read the covariate argument.

Scope

• The closure simulates on the compiled native engine (CPU in the CRAN build) — no Python, no GPU required.
• One simulation is produced per call, matching RSiena’s simstats0c contract; batching happens across Robbins–Monro iterations, not within a call.
• The current bridge covers the structural and single-covariate effects named in effect_names; mapping an arbitrary siena07 effects object (multiple covariates, behavior co-evolution) onto the native descriptors is under development. For those models, use the standalone mom_estimate().

Which path to choose

Situation	Recommended path
Well-conditioned model, want maximum speed	standalone mom_estimate()
Nearly collinear / hard convergence	cusna_fran() + siena07()
Need RSiena’s exact estimator semantics	cusna_fran() + siena07()
Behavior co-evolution, multi-network	standalone mom_estimate() / mom_estimate_multinet()